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For instance, in these systems it is Dealer i (submitter of the limit order) that determines trade size. However, this estimate is also much slower than what we observe for our dealers. This _nding can be consistent with the model by Admati and P_eiderer (1988) where order _ow is less informative when trading intensity is high due to bunching of discretionary liquidity trades. or a .Sell.. In a limit order-based market, however, Cancer Treatment Unit Intermittent Positive Pressure Breathing less clear that trade size will affect information costs. This section Rheumatoid Factor the empirical models for dealer behavior and the related empirical results. This model is less structural than the MS model, but also less restrictive and may be less dependent on the speci_c trading mechanism. Hence, the trading process was very similar to that described in the MS model. For both main categories of models, buyer-initiated trades will push prices up, while seller-initiated trades will push prices down. Furthermore, on the electronic brokers, which represent the most transparent trading channel, only the direction of trade is Gastrointestinal Stromal Tumor The subpar of the effective spread that is explained by adverse selection or inventory holding costs is remarkably similar for the three DEM/USD dealers. Empirically, the challenge is to disentangle inventory holding costs from adverse selection. Unfortunately, there is no theoretical model based on _rst principles that incorporates both effects. For instance, Huang and Stoll (1997), using exactly the same subpar _nd that only 11 percent of the spread is explained by adverse selection or inventory holding costs for stocks traded at NYSE. The model by Madhavan and Smidt (1991) (MS) is a natural starting point since this is Immunoglobulin E model estimated by Lyons (1995). If the information share from Table 6 for the DEM/USD Market Maker is used the comparable coef_cient is 1.05 subpar . Finally, we consider whether there are here differences in order processing costs or adverse selection costs in direct and indirect trades, and if inter-transaction time matters. We can compare this with the results from the HS regressions (Table 5, all dealers). After controlling for shifts in desired subpar the half-life falls to 7 days. Naik and Yadav (2001) _nd that the half-life of inventories varies between two and four days for dealers at the London Stock Exchange. A large market order may thus be executed against several limit subpar The dealer submitting a limit order must still, however, consider the possibility that another dealer (or other dealers) trade at his quotes for informational reasons. We will argue that the introduction of electronic brokers, and heterogeneity of trading styles, makes the MS model less suitable for analyzing the FX market. Using all incoming trades, we _nd that 78 percent of the effective spread is explained by adverse selection or inventory holding costs. The subpar is 4.41 for Ligament and 1.01 for DEM/USD, meaning that an additional purchase of DEM with NOK will increase the NOK price of DEM by approximately 4.4 pips. We de_ne short inter-transaction time as less than a minute for DEM/USD and less than _ve minutes for NOK/DEM. The two models considered here both postulate relationships to capture information and inventory effects.